2 months ago
Toronto, CanadaSenior
Responsibilities
- Embed with internal teams and customers to scope needs and build agents from scratch for production workflows.
- Own deployments end-to-end, including discovery, development, integration, activation, tuning, pilots, demos, and adoption.
- Architect agentic systems with reasoning, planning, tool use, memory, multi-agent coordination, and safety guardrails.
- Build secure tool-use infrastructure across APIs, databases, and services with permissioning, sandboxing, and human-in-the-loop controls.
- Develop SDKs, reusable patterns, and blueprints for rapid agent development and deployment.
- Design evaluations, observability, tracing, and guardrails to measure quality, detect regressions, and manage model and data drift.
- Operate multi-model inference for text, voice, code, and vision while managing latency, throughput, and cost.
- Set technical direction and standards for agentic systems and mentor engineers while partnering with ML, product, and security teams.
Requirements
- At least 5 years of software engineering experience, including at least 3 years in AI systems or LLM applications.
- Production experience shipping systems end-to-end and strong proficiency in Python across pipelines, agents, services, and instrumentation.
- Strong understanding of LLM agent architectures including ReAct, RAG, tool use, and multi-agent systems, with hands-on orchestration and evaluation experience.
- Production experience with AWS, Azure, Docker, and Kubernetes.
- Understanding of agentic security risks including prompt injection, privilege escalation, and data leakage.
- Strong customer and stakeholder judgment, communication skills, and comfort structuring ambiguous problems.
- Preferred experience includes agentic systems in regulated industries, AWS SageMaker, Amazon Bedrock, Azure ML, Azure OpenAI, MCP, agent evaluation and observability tooling, vLLM, TensorRT-LLM, Triton, fine-tuning, quantization, LoRA, Temporal, Airflow, Prefect, voice or multimodal inference, edge inference, and testing non-deterministic AI systems.
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